{"doi":"10.1016/j.semarthrit.2022.152140","title":"Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database","abstract":"OBJECTIVES: Osteoarthritis (OA) is a complex disease comprising diverse underlying patho-mechanisms. To enable the development of effective therapies, segmentation of the heterogenous patient population is critical. This study aimed at identifying such patient clusters using two different machine learning algorithms. METHODS: Using the progression and incident cohorts of the Osteoarthritis Initiative (OAI) dataset, deep embedded clustering (DEC) and multiple factor analysis with clustering (MFAC) approaches, including 157 input-variables at baseline, were employed to differentiate specific patient profiles. RESULTS: DEC resulted in 5 and MFAC in 3 distinct patient phenotypes. Both identified a \"comorbid\" cluster with higher body mass index (BMI), relevant burden of comorbidity and low levels of physical activity. Both methods also identified a younger and physically more active cluster and an elderly cluster with functional limitations, but low disease impact. The additional two clusters identified with DEC were subgroups of the young/physically active and the elderly/physically inactive clusters. Overall pain trajectories over 9 years were stable, only the numeric rating scale (NRS) for pain showed distinct increase, while physical activity decreased in all clusters. Clusters showed different (though non-significant) trajectories of joint space changes over the follow-up period of 8 years. CONCLUSION: Two different clustering approaches yielded similar patient allocations primarily separating complex \"comorbid\" patients from healthier subjects, the latter divided in young/physically active vs elderly/physically inactive subjects. The observed association to clinical (pain/physical activity) and structural progression could be helpful for early trial design as strategy to enrich for patients who may specifically benefit from disease-modifying treatments.","journal":"Seminars in Arthritis and Rheumatism","year":2022,"id":250214,"datarank":1.524820856897728,"base_score":3.332204510175204,"endowment":3.332204510175204,"self_citation_contribution":0.49983067652628066,"citation_network_contribution":1.0249901803714474,"self_endowment_contribution":0.49983067652628066,"citer_contribution":1.0249901803714474,"corpus_percentile":86.43149996132126,"corpus_rank":1755,"citation_count":27,"citer_count":19,"citers_with_citation_signal":14,"citers_with_endowment":14,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7127,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":891797,"name":"Franziska Saxer","orcid":"0000-0003-4153-6795","position":1,"is_corresponding":false},{"id":892372,"name":"P Lustenberger","orcid":null,"position":2,"is_corresponding":false},{"id":687411,"name":"László B. Tankó","orcid":null,"position":3,"is_corresponding":false},{"id":892373,"name":"Philipp Nikolaus","orcid":null,"position":4,"is_corresponding":false},{"id":892374,"name":"Ilja Rasin","orcid":null,"position":5,"is_corresponding":false},{"id":892375,"name":"Damian F. Brennan","orcid":null,"position":6,"is_corresponding":false},{"id":88867,"name":"Ronenn Roubenoff","orcid":"0000-0002-3959-3179","position":7,"is_corresponding":false},{"id":892376,"name":"Sumehra Premji","orcid":null,"position":8,"is_corresponding":false},{"id":91545,"name":"Philip G. Conaghan","orcid":"0000-0002-3478-5665","position":9,"is_corresponding":false},{"id":891798,"name":"Matthias Schieker","orcid":"0000-0002-1230-3653","position":10,"is_corresponding":false},{"id":891796,"name":"David Demanse","orcid":"0000-0003-1927-6720","position":0,"is_corresponding":true}],"reference_count":73,"raw_metadata":null,"created_at":"2026-07-19T00:24:28.242323Z","pmid":"36446256","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}